Building profile extraction is crucial in three-dimensional urban reconstruction. The traditional Alpha-shapes profile extraction algorithm has strong robustness and easy implementation, but the extracted profile is easily interfered by noise points, making it difficult to obtain an accurate profile edge. This paper proposes an improved Alpha-shapes profile extraction algorithm to solve the abovementioned problem. First, the initial profile points extracted via the Alpha-shapes algorithm are selected by a random sample consensus algorithm. Thereafter, the key profile points are determined using the Douglas-Peucker algorithm. Finally, an accurate profile is extracted through forced orthogonal optimization. Three groups of point clouds with different building shapes are used for experimental analysis. The experimental results demonstrate that the algorithm is superior to the traditional Alpha-shapes algorithm as the improved algorithm can obtain more accurate building edges and effectively overcome the jagged edges of the traditional Alpha-shapes algorithm, and the accuracy, completeness and quality are also better than those of the traditional Alpha-shapes algorithm.